High-resolution and continuous soil maps are an essential prerequisite for precision agriculture and many environmental studies. Conventional, sample-based soil mapping is costly and time consuming, and the data collected are available only for discrete points in any landscape. Thus, sample-based soil mapping is not reasonably applicable for large areas like countries. Due to these limitations, geostatistical techniques can be used to map soil properties. Soil organic carbon (SOC) is one of the most important parameters shaping soil environment and it plays a key role in determining soil quality. Spatial prediction of soil organic carbon in a large scale has an important role in environmental studies and field practices for both soil quality and carbon sequestration. This study was conducted to interpolation of point data to produce continuous map of soil organic carbon content in Slovakia. The measured point data were extracted from LUCAS (Land Use/Cover Area Frame Survey) results for Slovakia region. The regression kriging approach is applied and Corine Land Cover data (CLC), SRTM 90m, European Soil Database (ESDB), climate, Land Management data were used as covariates. Finally, the soil organic carbon prediction map was produced in raster format at a spatial resolution of 100×100m.